Web-Based Intervention Effects on Mild Cognitive Impairment Based on Apolipoprotein E Genotype: Quasi-Experimental Study.

BACKGROUND: Apolipoprotein E (APOE) ε4 allele is a major genetic risk factor for Alzheimer disease and mild cognitive impairment (MCI). Computer-based training programs can improve cognitive performance in elderly populations. However, the effects of computer-based interventions on MCI APOE ε4 carriers have never been studied before. OBJECTIVE: The effects of different web-based interventions and the APOE isoform-specific differences in training outcomes are investigated. METHODS: Using a quasi-experimental study design, 202 participants with MCI aged 60 years and older took part in three different intervention programs (physical and cognitive [Long-Lasting Memories, or LLM], cognitive [Active Control, or AC], or physical intervention [Physical Training Control, or PTC]) via an innovative information and communication technologies exergaming platform. Participants in each interventional group were subdivided into APOE ε4 carriers and non-APOE ε4 carriers. All participants underwent an extensive neuropsychological evaluation before and after the training, blood tests, and brain imaging. RESULTS: All interventions resulted in multiple statistically significant cognitive benefits after the intervention. Verbal learning (California Verbal Learning Test: immediate recall test score-LLM: P=.04; AC: P<.001), working memory (digit span forward and backward test scores-AC: P=.03; PTC: P=.02 and P=.006, respectively), and long-term memory (California Verbal Learning Test: delayed recall test score-LLM: P=.02; AC: P=.002; and PTC: P=.02) were improved. There was no statistically significant difference among the intervention effects. APOE ε4 presence moderates intervention effects as the LLM intervention improved only their task-switching processing speed (Trail Making Test, Part B: P=.03) and the PTC intervention improved only the working memory (digit span backward: P=.03). No significant performance alteration was noted for the APOE ε4+ cognitive AC training group. CONCLUSIONS: None of the applied interventions could be identified as the optimal one; it is suggested, however, that combined cognitive and physical training and physical training via exergaming may be more effective for the high-risk MCI ΑPOE ε4+ subgroup.

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury.

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

Does combined training of biofeedback and neurofeedback affect smoking status, behavior, and longitudinal brain plasticity?

Introduction: Investigations of biofeedback (BF) and neurofeedback (NF) training for nicotine addiction have been long documented to lead to positive gains in smoking status, behavior and to changes in brain activity. We aimed to: (a) evaluate a multi-visit combined BF/NF intervention as an alternative smoking cessation approach, (b) validate training-induced feedback learning, and (c) document effects on resting-state functional connectivity networks (rsFCN); considering gender and degree of nicotine dependence in a longitudinal design. Methods: We analyzed clinical, behavioral, and electrophysiological data from 17 smokers who completed five BF and 20 NF sessions and three evaluation stages. Possible neuroplastic effects were explored comparing whole-brain rsFCN by phase-lag index (PLI) for different brain rhythms. PLI connections with significant change across time were investigated according to different resting-state networks (RSNs). Results: Improvements in smoking status were observed as exhaled carbon monoxide levels, Total Oxidative Stress, and Fageström scores decreased while Vitamin E levels increased across time. BF/NF promoted gains in anxiety, self-esteem, and several aspects of cognitive performance. BF learning in temperature enhancement was observed within sessions. NF learning in theta/alpha ratio increase was achieved across baselines and within sessions. PLI network connections significantly changed across time mainly between or within visual, default mode and frontoparietal networks in theta and alpha rhythms, while beta band RSNs mostly changed significantly after BF sessions. Discussion: Combined BF/NF training positively affects the clinical and behavioral status of smokers, displays benefit in smoking harm reduction, plays a neuroprotective role, leads to learning effects and to positive reorganization of RSNs across time. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02991781.

Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: Protocol for a Nonrandomized Controlled Pilot Study.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior. OBJECTIVE: To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user's likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management. METHODS: A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed ("Pizza on Time") for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants' parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player's likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed. RESULTS: A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users' labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform. CONCLUSIONS: ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. TRIAL REGISTRATION: ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/40189.

Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.

BACKGROUND: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system. OBJECTIVE: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine. METHODS: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023. RESULTS: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024. CONCLUSIONS: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/41152.

Assessment of the Safety Signal for the Abuse Potential of Pregabalin and Gabapentin Using the FAERS Database and Big Data Search Analytics.

Introduction: The latest decade, an emerging issue has been the abuse potential of the gabapentinoids pregabalin and gabapentin. The aim of our study was to assess this safety signal combining two different methods of surveillance: search analytics big data and the FDA spontaneous reporting system database. Methods: Analysis of big data and the FAERS was used to detect pregabalin's and gabapentin's abuse potential in comparison with two controls, clonazepam and levetiracetam, and further, the correlation between these domains was investigated. Data from the United States between 2007 and 2020Q2 were analyzed. Results: The FAERS analysis revealed the following pattern of signals: clonazepam > pregabalin ≥ gabapentin > levetiracetam, for both the primary term "drug abuse and dependence" and the secondary terms (withdrawal, tolerance, overdose). The Google domain pattern was slightly different: clonazepam ≥ gabapentin ≥ pregabalin≥ levetiracetam. A monotonic correlation was found between FAERS and Google searches for gabapentin (r = 0.558; p < 0.001), pregabalin (r = 0.587; p < 0.001), and clonazepam (r = 0.295; p = 0.030). Conclusion: Our results revealed that there is preliminary evidence of a safety signal for the abuse potential of pregabalin and gabapentin. Analysis of the FAERS database, supplemented by big data search analytics, suggests that there is potential of using these methods as a supplementary tool to detect drug abuse-related safety signals in pharmacovigilance.

Development of Cognitive and Physical Exercise Systems, Clinical Recordings, Large-Scale Data Analytics, and Virtual Coaching for Heart Failure Patients: Protocol for the BioTechCOACH-ForALL Project.

BACKGROUND: Heart failure is a chronic disease affecting patient morbidity and mortality. Current guidelines for heart failure patient treatment are focused on improving their clinical status, functional capacity, and quality of life. However, these guidelines implement numerous instructions including medical treatment adherence, physical activity, and self-care management. The complexity of the therapeutic instructions makes them difficult to follow especially by older adults. OBJECTIVE: The challenge of this project is to (1) measure real-life adherence to a regular physical exercise program and (2) attempt to influence older adult patients with heart failure toward embracing a more physically active self-care lifestyle. METHODS: This research consists of two studies, including a lab experiment and a pragmatic evaluation of technology at patients' homes. The lab experiment aims at exploring in an objective way (measuring neurophysiological responses to stimuli) patient engagement with different characteristics of virtual agents, while the home study is a 3-phase prospective study where the developed technology platform is tested by heart failure patients in their own home environments. Patients undergo evaluation of their physical activity and cognitive status using standard evaluation methods (6-minute walk test, questionnaires) and receive wearable devices to accurately measure everyday life activity levels (home study phases 1-3). During home study phases 2 and 3, exergames (serious games for physical exercise) to provide a physical exercise plan as a joyful activity are delivered to patients' private households and e-coaching techniques are implemented in the final phase (home study phase 3) of the protocol, to influence patient attitudes toward a more healthy and recommended lifestyle. RESULTS: The trial is still ongoing. Recruitment is ongoing, and the project has progressed for some participants through phase 2 of the home study. The sample size for both studies is 28 participants; 10 have already been included in the study, and both baseline clinical and patient-reported outcome data are retrieved. Phases 2 and 3 of the home pilot study are expected to be completed within 6 months. CONCLUSIONS: The main challenge of the project is the change of attitude of older age heart failure patients through an e-coaching system. Given the adoption of a cocreation and living lab approach and the main objective for real-life evaluation, the project is ready to react to any collected feedback, even during the implementation of the research plan. Clinical assessment and objective evaluation are expected to provide all required information for reliable findings. TRIAL REGISTRATION: ClinicalTrials.gov NCT03877328; https://clinicaltrials.gov/ct2/show/NCT03877328. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17714.

Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study.

BACKGROUND: The role of emotion is crucial to the learning process, as it is linked to motivation, interest, and attention. Affective states are expressed in the brain and in overall biological activity. Biosignals, like heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG) are physiological expressions affected by emotional state. Analyzing these biosignal recordings can point to a person's emotional state. Contemporary medical education has progressed extensively towards diverse learning resources using virtual reality (VR) and mixed reality (MR) applications. OBJECTIVE: This paper aims to study the efficacy of wearable biosensors for affect detection in a learning process involving a serious game in the Microsoft HoloLens VR/MR platform. METHODS: A wearable array of sensors recording HR, EDA, and EEG signals was deployed during 2 educational activities conducted by 11 participants of diverse educational level (undergraduate, postgraduate, and specialist neurosurgeon doctors). The first scenario was a conventional virtual patient case used for establishing the personal biosignal baselines for the participant. The second was a case in a VR/MR environment regarding neuroanatomy. The affective measures that we recorded were EEG (theta/beta ratio and alpha rhythm), HR, and EDA. RESULTS: Results were recorded and aggregated across all 3 groups. Average EEG ratios of the virtual patient (VP) versus the MR serious game cases were recorded at 3.49 (SD 0.82) versus 3.23 (SD 0.94) for students, 2.59 (SD 0.96) versus 2.90 (SD 1.78) for neurosurgeons, and 2.33 (SD 0.26) versus 2.56 (SD 0.62) for postgraduate medical students. Average alpha rhythm of the VP versus the MR serious game cases were recorded at 7.77 (SD 1.62) µV versus 8.42 (SD 2.56) µV for students, 7.03 (SD 2.19) µV versus 7.15 (SD 1.86) µV for neurosurgeons, and 11.84 (SD 6.15) µV versus 9.55 (SD 3.12) µV for postgraduate medical students. Average HR of the VP versus the MR serious game cases were recorded at 87 (SD 13) versus 86 (SD 12) bpm for students, 81 (SD 7) versus 83 (SD 7) bpm for neurosurgeons, and 81 (SD 7) versus 77 (SD 6) bpm for postgraduate medical students. Average EDA of the VP versus the MR serious game cases were recorded at 1.198 (SD 1.467) µS versus 4.097 (SD 2.79) µS for students, 1.890 (SD 2.269) µS versus 5.407 (SD 5.391) µS for neurosurgeons, and 0.739 (SD 0.509) µS versus 2.498 (SD 1.72) µS for postgraduate medical students. The variations of these metrics have been correlated with existing theoretical interpretations regarding educationally relevant affective analytics, such as engagement and educational focus. CONCLUSIONS: These results demonstrate that this novel sensor configuration can lead to credible affective state detection and can be used in platforms like intelligent tutoring systems for providing real-time, evidence-based, affective learning analytics using VR/MR-deployed medical education resources.

Clinical factors affecting the survival of patients diagnosed with non-small cell lung cancer and metastatic malignant pleural effusion, treated with hyperthermic intrathoracic chemotherapy or chemical talc pleurodesis: a monocentric, prospective, randomized trial.

BACKGROUND: There is a plethora of treatment algorithms for managing patients with malignant pleural effusions (MPEs), sharing many common points and principles. Our study aims to compare hyperthermic intrapleural chemotherapy (HITHOC) and talc pleurodesis (TALC), as treatment options for patients with non-small cell lung cancer (NSCLC) and metastatic MPE. METHODS: This prospective, randomized trial was conducted at a single thoracic surgery center, the "Theagenio" Cancer Institute, in Greece, under the identification code NCT01409551 and was completed. All 40 patients enrolled were adults with histologically proven metastatic, unilateral, MPE caused by NSCLC. Exclusion criteria included patients >80 years, trapped lung, and major comorbidities. Patients were randomly and equally assigned 1:1 to either HITHOC (group A) or TALC (group B) by video assisted thoracic surgery (VATS). The primary outcome was the median overall survival (OS) from trial intervention to death, while secondary outcome was the identification of clinical factors affecting the survival. RESULTS: The patients were followed up for 45 months. The OS of the full group was 8 months (95% CI: 7.046-8.954). Participants who underwent HITHOC had an OS of 8 months (95% CI: 7.141-8.859), whereas the participants of TALC had an OS of 9 months (95% CI: 7.546-10.454), with no significant difference between groups. Among fifty-four factors that were tested for their effects on survival, only TNM stage and creatinine values both preoperatively and 7 days postoperatively could be regarded as risk-factors for survival. Other recorded parameters, which had significant variance between the two groups, were urea levels, C-reactive protein, white blood cells and total in hospital length of stay (LOS). CONCLUSIONS: Both HITHOC and TALC are equally effective and safe therapeutic options in treating patients with MPE and NSCLC with acceptable survival. The study revealed independent clinical risk factors influencing survival, which could be utilized as starting points for larger clinical studies. KEYWORDS: Pleurodesis; pleural effusion; malignant; carcinoma; non-small cell lung; hyperthermia.

Resting-state Abnormalities in Heroin-dependent Individuals.

Drug addiction is a major health problem worldwide. Recent neuroimaging studies have shed light into the underlying mechanisms of drug addiction as well as its consequences to the human brain. The most vulnerable, to heroin addiction, brain regions have been reported to be specific prefrontal, parietal, occipital, and temporal regions, as well as, some subcortical regions. The brain regions involved are usually linked with reward, motivation/drive, memory/learning, inhibition as well as emotional control and seem to form circuits that interact with each other. So, along with neuroimaging studies, recent advances in resting-state dynamics might allow further assessments upon the multilayer complexity of addiction. In the current manuscript, we comprehensively review and discuss existing resting-state neuroimaging findings classified into three overlapping and interconnected groups: functional connectivity alterations, structural deficits and abnormal topological properties. Moreover, behavioral traits of heroin-addicted individuals as well as the limitations of the currently available studies are also reviewed. Finally, in need of a contemporary therapy a multimodal therapeutic approach is suggested using classical treatment practices along with current neurotechonologies, such as neurofeedback and goal-oriented video-games.

Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space.

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

Exploring the Neuroplastic Effects of Biofeedback Training on Smokers.

Smoking and stress cooccur in different stages of a nicotine addiction cycle, affecting brain function and showing additive impact on different physiological responses. Resting-state functional connectivity has shown potential in identifying these alterations. Nicotine addiction has been associated with detrimental effects on functional integrity of the central nervous system, including the organization of resting-state networks. Prolonged stress may result in enhanced activation of the default mode network (DMN). Considering that biofeedback has shown promise in alleviating physiological manifestations of stress, we aimed to explore the possible neuroplastic effects of biofeedback training on smokers. Clinical, behavioral, and neurophysiological (resting-state EEG) data were collected from twenty-seven subjects before and after five sessions of skin temperature training. DMN functional cortical connectivity was investigated. While clinical status remained unaltered, the degree of nicotine dependence and psychiatric symptoms were significantly improved. Significant changes in DMN organization and network properties were not observed, except for a significant increase of information flow from the right ventrolateral prefrontal cortex and right temporal pole cortex towards other DMN components. Biofeedback aiming at stress alleviation in smokers could play a protective role against maladaptive plasticity of connectivity. Multiple sessions, individualized interventions and more suitable methods to promote brain plasticity, such as neurofeedback training, should be considered.

Math anxiety: Brain cortical network changes in anticipation of doing mathematics.

Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.

A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury.

Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms.

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.